Harishwar Reddy K

Ph.D. Researcher · Artificial Intelligence

Harishwar Reddy K

Machine Learning · Computer Vision · Foundation Models · Multimodal AI

I am a Ph.D. student in Electrical and Computer Engineering at the University of Florida. I develop scalable machine learning systems for visual representation learning, foundation models, explainable AI, multimodal learning, biomedical imaging, and large-scale prediction problems.

PyTorch Computer Vision Self-Supervised Learning Foundation Models VLMs & LLMs MLOps CUDA & SLURM Biomedical AI
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About

AI research with strong applied engineering

My primary research interests include computer vision, foundation models, self-supervised learning, explainable AI, digital pathology, multi-omics, and clinical prediction. I also have experience applying vision-language models and large language models to multimodal AI, content analysis, and facial attribute recognition.

My work combines research and engineering: model development, large-scale experimentation, reproducible evaluation, workflow automation, GPU computing, data pipelines, and collaboration with domain experts.

Research roles Research Scientist, Applied Scientist, ML Research, and Computer Vision.
Engineering roles Machine Learning Engineer, AI Engineer, MLOps, and model evaluation.
Application areas Biomedical AI, digital pathology, multimodal systems, and visual analytics.

Experience

Research and industry experience

Aug 2023 — Present
Gainesville, FL

Graduate Research Assistant · University of Florida

  • Pretrained a kidney-specific multi-stain, multi-resolution histopathology foundation model on approximately 30,000 whole-slide images.
  • Built representation learning, multiple instance learning, clinical prediction, and explainable AI pipelines.
  • Developed an open-source Python framework benchmarking 11 foundation models across 11 downstream tasks.
  • Built scalable segmentation, cell detection, and quantitative feature extraction systems for gigapixel imagery.
Apr 2023 — Jul 2023
Bangalore, India

Research Intern · Siemens

  • Developed an explainable active learning framework for industrial computer vision using MLflow and Prefect.
  • Fine-tuned deep neural networks for multiclass object classification.
  • Designed sample-selection strategies to reduce annotation effort and improve model interpretability.
Jul 2022 — Mar 2023
Bangalore, India

Junior Research Fellow · Indian Institute of Science

  • Conducted research in computer vision, medical image analysis, image denoising, and explainable AI.
  • Evaluated Layer-CAM, Eigen-CAM, and HiResCAM with Inception-ResNet-v2 for OCT abnormality analysis.
  • First-authored work presented through medical imaging venues.

Projects

Selected AI and machine learning work

Histopathology Foundation Model

Large-scale self-supervised pretraining across multiple stains and image resolutions using approximately 30,000 whole-slide images.

PyTorch · Vision Transformers · Distributed Computing · HPC

Foundation Model Benchmarking Framework

Reproducible Python framework for comparing 11 foundation models across classification, regression, detection, and prognosis tasks.

Python · Model Evaluation · Scikit-learn · Reproducibility

Explainable Active Learning

Active-learning and workflow-orchestration system for industrial computer vision with explainability and experiment tracking.

Deep Learning · MLflow · Prefect · MLOps

Clinical Prediction and Pathomics

Interpretable machine learning pipelines for treatment response, prognosis, biomarker discovery, and quantitative image analysis.

MIL · Feature Engineering · XAI · Clinical AI

Vision-Language and Large Language Models

Research on facial attribute recognition, multimodal content analysis, online toxicity, and sensitive-content detection.

VLMs · LLMs · Multimodal AI · Responsible AI

Medical Image Analysis

Deep learning, denoising, volumetric analysis, and explainability for optical coherence tomography and histopathology.

CNNs · CAM Methods · Segmentation · Image Processing

Publications

Research publications

Complete list of distinct scholarly works indexed across my public research profiles. Published versions are shown instead of duplicate preprint records when both exist.

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Skills

Technical skills

Programming & ML

Python, PyTorch, Scikit-learn, Hugging Face, NumPy, Pandas, MATLAB

AI Methods

Computer Vision, Foundation Models, Self-Supervised Learning, Multiple Instance Learning, VLMs, LLMs, Explainable AI

Engineering & Infrastructure

Git, Docker, Linux, CUDA, SLURM, Jupyter, MLflow, Prefect, MLOps

Curriculum Vitae

CV

Open CV Download PDF

Contact

Open to AI research and engineering opportunities

I am interested in research scientist, applied scientist, machine learning engineer, computer vision, foundation-model, multimodal AI, MLOps, and biomedical AI opportunities.

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